mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-11-18 01:06:41 +00:00
541 lines
23 KiB
Cython
541 lines
23 KiB
Cython
# cython: infer_types=True
|
|
# cython: profile=False
|
|
|
|
cimport cython
|
|
import numpy as np
|
|
from libc.stdlib cimport malloc, free
|
|
|
|
|
|
# TODO: use a cdef class
|
|
# C representation of dither_pattern.DitherPattern data, for efficient access.
|
|
cdef struct Dither:
|
|
|
|
float* pattern # Flattened dither pattern
|
|
int x_shape
|
|
int y_shape
|
|
int x_origin
|
|
int y_origin
|
|
|
|
|
|
cdef float clip(float a, float min_value, float max_value) nogil:
|
|
return min(max(a, min_value), max_value)
|
|
|
|
|
|
# Compute left-hand bounding box for dithering at horizontal position x.
|
|
cdef int dither_bounds_xl(Dither *dither, int x) nogil:
|
|
cdef int el = max(dither.x_origin - x, 0)
|
|
cdef int xl = x - dither.x_origin + el
|
|
return xl
|
|
|
|
|
|
#Compute right-hand bounding box for dithering at horizontal position x.
|
|
cdef int dither_bounds_xr(Dither *dither, int x_res, int x) nogil:
|
|
cdef int er = min(dither.x_shape, x_res - x)
|
|
cdef int xr = x - dither.x_origin + er
|
|
return xr
|
|
|
|
|
|
# Compute upper bounding box for dithering at vertical position y.
|
|
cdef int dither_bounds_yt(Dither *dither, int y) nogil:
|
|
cdef int et = max(dither.y_origin - y, 0)
|
|
cdef int yt = y - dither.y_origin + et
|
|
|
|
return yt
|
|
|
|
|
|
# Compute lower bounding box for dithering at vertical position y.
|
|
cdef int dither_bounds_yb(Dither *dither, int y_res, int y) nogil:
|
|
cdef int eb = min(dither.y_shape, y_res - y)
|
|
cdef int yb = y - dither.y_origin + eb
|
|
return yb
|
|
|
|
|
|
cdef inline unsigned char shift_pixel_window(
|
|
unsigned char last_pixels,
|
|
unsigned int next_pixels,
|
|
unsigned char shift_right_by,
|
|
unsigned char window_width) nogil:
|
|
"""Right-shift a sliding window of n pixels to incorporate new pixels.
|
|
|
|
Args:
|
|
last_pixels: n-bit value representing n pixels from left up to current position (MSB = current pixel).
|
|
next_pixels: n-bit value representing n pixels to right of current position (LSB = pixel to right)
|
|
shift_right_by: how many pixels of next_pixels to shift into the sliding window
|
|
window_width: how many pixels to maintain in the sliding window (must be <= 8)
|
|
|
|
Returns: n-bit value representing shifted pixel window
|
|
"""
|
|
cdef unsigned char window_mask = 0xff >> (8 - window_width)
|
|
cdef unsigned int shifted_next_pixels
|
|
|
|
if window_width > shift_right_by:
|
|
shifted_next_pixels = next_pixels << (window_width - shift_right_by)
|
|
else:
|
|
shifted_next_pixels = next_pixels >> (shift_right_by - window_width)
|
|
return ((last_pixels >> shift_right_by) | shifted_next_pixels) & window_mask
|
|
|
|
|
|
# Look ahead a number of pixels and compute choice for next pixel with lowest total squared error after dithering.
|
|
#
|
|
# Args:
|
|
# dither: error diffusion pattern to apply
|
|
# palette_rgb: matrix of all n-bit colour palette RGB values
|
|
# image_rgb: RGB image in the process of dithering
|
|
# x: current horizontal screen position
|
|
# y: current vertical screen position
|
|
# options_nbit: matrix of (2**lookahead, lookahead) possible n-bit colour choices at positions x .. x + lookahead
|
|
# lookahead: how many horizontal pixels to look ahead
|
|
# distances: matrix of (24-bit RGB, n-bit palette) perceptual colour distances
|
|
# x_res: horizontal screen resolution
|
|
#
|
|
# Returns: index from 0 .. 2**lookahead into options_nbit representing best available choice for position (x,y)
|
|
#
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_cam16, float[:, :, ::1] palette_rgb,
|
|
float[:, :, ::1] image_rgb, int x, int y, int lookahead, unsigned char last_pixels,
|
|
int x_res, float[:,::1] rgb_to_cam16ucs, unsigned char palette_depth) nogil:
|
|
cdef int candidate_pixels, i, j
|
|
cdef float[3] quant_error
|
|
cdef int best
|
|
cdef float best_error = 2**31-1
|
|
cdef float total_error
|
|
cdef unsigned char next_pixels
|
|
cdef int phase
|
|
cdef float[::1] lah_cam16ucs
|
|
|
|
# Don't bother dithering past the lookahead horizon or edge of screen.
|
|
cdef int xxr = min(x + lookahead, x_res)
|
|
|
|
cdef int lah_shape1 = xxr - x
|
|
cdef int lah_shape2 = 3
|
|
cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
|
|
|
|
# For each 2**lookahead possibilities for the on/off state of the next lookahead pixels, apply error diffusion
|
|
# and compute the total squared error to the source image. Since we only have two possible colours for each
|
|
# given pixel (dependent on the state already chosen for pixels to the left), we need to look beyond local minima.
|
|
# i.e. it might be better to make a sub-optimal choice for this pixel if it allows access to much better pixel
|
|
# colours at later positions.
|
|
for candidate_pixels in range(1 << lookahead):
|
|
# Working copy of input pixels
|
|
for i in range(xxr - x):
|
|
for j in range(3):
|
|
lah_image_rgb[i * lah_shape2 + j] = image_rgb[y, x+i, j]
|
|
|
|
total_error = 0
|
|
# Apply dithering to lookahead horizon or edge of screen
|
|
for i in range(xxr - x):
|
|
xl = dither_bounds_xl(dither, i)
|
|
xr = dither_bounds_xr(dither, xxr - x, i)
|
|
phase = (x + i) % 4
|
|
|
|
next_pixels = shift_pixel_window(
|
|
last_pixels, next_pixels=candidate_pixels, shift_right_by=i+1, window_width=palette_depth)
|
|
|
|
# We don't update the input at position x (since we've already chosen fixed outputs), but we do propagate
|
|
# quantization errors to positions >x so we can compensate for how good/bad these choices were. i.e. the
|
|
# next_pixels choices are fixed, but we can still distribute quantization error from having made these
|
|
# choices, in order to compute the total error.
|
|
for j in range(3):
|
|
quant_error[j] = lah_image_rgb[i * lah_shape2 + j] - palette_rgb[next_pixels, phase, j]
|
|
apply_one_line(dither, xl, xr, i, lah_image_rgb, lah_shape2, quant_error)
|
|
|
|
lah_cam16ucs = convert_rgb_to_cam16ucs(
|
|
rgb_to_cam16ucs, lah_image_rgb[i*lah_shape2], lah_image_rgb[i*lah_shape2+1],
|
|
lah_image_rgb[i*lah_shape2+2])
|
|
total_error += colour_distance_squared(lah_cam16ucs, palette_cam16[next_pixels, phase])
|
|
|
|
if total_error >= best_error:
|
|
# No need to continue
|
|
break
|
|
|
|
if total_error < best_error:
|
|
best_error = total_error
|
|
best = candidate_pixels
|
|
|
|
free(lah_image_rgb)
|
|
return best
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef inline float[::1] convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil:
|
|
cdef int rgb_24bit = (<int>(r*255) << 16) + (<int>(g*255) << 8) + <int>(b*255)
|
|
return rgb_to_cam16ucs[rgb_24bit]
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef inline float fabs(float value) nogil:
|
|
return -value if value < 0 else value
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef inline float colour_distance_squared(float[::1] colour1, float[::1] colour2) nogil:
|
|
return (colour1[0] - colour2[0]) ** 2 + (colour1[1] - colour2[1]) ** 2 + (colour1[2] - colour2[2]) ** 2
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef inline float colour_distance(float[::1] colour1, float[::1] colour2) nogil:
|
|
return fabs(colour1[0] - colour2[0]) + fabs(colour1[1] - colour2[1]) + fabs(colour1[2] - colour2[2])
|
|
|
|
|
|
# Perform error diffusion to a single image row.
|
|
#
|
|
# Args:
|
|
# dither: dither pattern to apply
|
|
# xl: lower x bounding box
|
|
# xr: upper x bounding box
|
|
# x: starting horizontal position to apply error diffusion
|
|
# image: array of shape (image_shape1, 3) representing RGB pixel data for a single image line, to be mutated.
|
|
# image_shape1: horizontal dimension of image
|
|
# quant_error: RGB quantization error to be diffused
|
|
#
|
|
cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, int image_shape1,
|
|
float[] quant_error) nogil:
|
|
|
|
cdef int i, j
|
|
cdef float error_fraction
|
|
|
|
for i in range(xl, xr):
|
|
error_fraction = dither.pattern[i - x + dither.x_origin]
|
|
for j in range(3):
|
|
image[i * image_shape1 + j] = clip(image[i * image_shape1 + j] + error_fraction * quant_error[j], 0, 1)
|
|
|
|
|
|
# Perform error diffusion across multiple image rows.
|
|
#
|
|
# Args:
|
|
# dither: dither pattern to apply
|
|
# x_res: horizontal image resolution
|
|
# y_res: vertical image resolution
|
|
# x: starting horizontal position to apply error diffusion
|
|
# y: starting vertical position to apply error diffusion
|
|
# image: RGB pixel data, to be mutated
|
|
# quant_error: RGB quantization error to be diffused
|
|
#
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::1] image, float[] quant_error) nogil:
|
|
|
|
cdef int i, j, k
|
|
|
|
cdef int yt = dither_bounds_yt(dither, y)
|
|
cdef int yb = dither_bounds_yb(dither, y_res, y)
|
|
cdef int xl = dither_bounds_xl(dither, x)
|
|
cdef int xr = dither_bounds_xr(dither, x_res, x)
|
|
|
|
cdef float error_fraction
|
|
for i in range(yt, yb):
|
|
for j in range(xl, xr):
|
|
error_fraction = dither.pattern[(i - y) * dither.x_shape + j - x + dither.x_origin]
|
|
for k in range(3):
|
|
image[i,j,k] = clip(image[i,j,k] + error_fraction * quant_error[k], 0, 1)
|
|
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef image_nbit_to_bitmap(
|
|
(unsigned char)[:, ::1] image_nbit, unsigned int x_res, unsigned int y_res, unsigned char palette_depth):
|
|
cdef unsigned int x, y
|
|
bitmap = np.zeros((y_res, x_res), dtype=bool)
|
|
for y in range(y_res):
|
|
for x in range(x_res):
|
|
# MSB of each array element is the pixel state at (x, y)
|
|
bitmap[y, x] = image_nbit[y, x] >> (palette_depth - 1)
|
|
return bitmap
|
|
|
|
|
|
# Dither a source image
|
|
#
|
|
# Args:
|
|
# screen: screen.Screen object
|
|
# image_rgb: input RGB image
|
|
# dither: dither_pattern.DitherPattern to apply during dithering
|
|
# lookahead: how many x positions to look ahead to optimize colour choices
|
|
# verbose: whether to output progress during image conversion
|
|
#
|
|
# Returns: tuple of n-bit output image array and RGB output image array
|
|
#
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def dither_image(
|
|
screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsigned char verbose, float[:,::1] rgb_to_cam16ucs):
|
|
cdef int y, x
|
|
cdef unsigned char i, j, pixels_nbit, phase
|
|
# cdef float[3] input_pixel_rgb
|
|
cdef float[3] quant_error
|
|
cdef unsigned char output_pixel_nbit
|
|
cdef unsigned char best_next_pixels
|
|
cdef float[3] output_pixel_rgb
|
|
|
|
# Hoist some python attribute accesses into C variables for efficient access during the main loop
|
|
|
|
cdef int yres = screen.Y_RES
|
|
cdef int xres = screen.X_RES
|
|
|
|
# TODO: convert this instead of storing on palette?
|
|
cdef float[:, :, ::1] palette_cam16 = np.zeros((len(screen.palette.CAM16UCS), 4, 3), dtype=np.float32)
|
|
for pixels_nbit, phase in screen.palette.CAM16UCS.keys():
|
|
for i in range(3):
|
|
palette_cam16[pixels_nbit, phase, i] = screen.palette.CAM16UCS[pixels_nbit, phase][i]
|
|
|
|
cdef float[:, :, ::1] palette_rgb = np.zeros((len(screen.palette.RGB), 4, 3), dtype=np.float32)
|
|
for pixels_nbit, phase in screen.palette.RGB.keys():
|
|
for i in range(3):
|
|
palette_rgb[pixels_nbit, phase, i] = screen.palette.RGB[pixels_nbit, phase][i] / 255
|
|
|
|
cdef Dither cdither
|
|
cdither.y_shape = dither.PATTERN.shape[0]
|
|
cdither.x_shape = dither.PATTERN.shape[1]
|
|
cdither.y_origin = dither.ORIGIN[0]
|
|
cdither.x_origin = dither.ORIGIN[1]
|
|
# TODO: should be just as efficient to use a memoryview?
|
|
cdither.pattern = <float *> malloc(cdither.x_shape * cdither.y_shape * sizeof(float))
|
|
for i in range(cdither.y_shape):
|
|
for j in range(cdither.x_shape):
|
|
cdither.pattern[i * cdither.x_shape + j] = dither.PATTERN[i, j]
|
|
|
|
cdef unsigned char palette_depth = screen.palette.PALETTE_DEPTH
|
|
|
|
# The nbit image representation contains the trailing n dot values as an n-bit value with MSB representing the
|
|
# current pixel. This choice (cf LSB) is slightly awkward but matches the DHGR behaviour that bit positions in
|
|
# screen memory map LSB to MSB from L to R. The value of n is chosen by the palette depth, i.e. how many trailing
|
|
# dot positions are used to determine the colour of a given pixel.
|
|
cdef (unsigned char)[:, ::1] image_nbit = np.empty((image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
|
|
|
|
for y in range(yres):
|
|
if verbose:
|
|
print("%d/%d" % (y, yres))
|
|
output_pixel_nbit = 0
|
|
for x in range(xres):
|
|
# Compute all possible 2**N choices of n-bit pixel colours for positions x .. x + lookahead
|
|
# lookahead_palette_choices_nbit = lookahead_options(lookahead, output_pixel_nbit)
|
|
# Apply error diffusion for each of these 2**N choices, and compute which produces the closest match
|
|
# to the source image over the succeeding N pixels
|
|
best_next_pixels = dither_lookahead(
|
|
&cdither, palette_cam16, palette_rgb, image_rgb, x, y, lookahead, output_pixel_nbit, xres,
|
|
rgb_to_cam16ucs, palette_depth)
|
|
# Apply best choice for next 1 pixel
|
|
output_pixel_nbit = shift_pixel_window(
|
|
output_pixel_nbit, best_next_pixels, shift_right_by=1, window_width=palette_depth)
|
|
|
|
# Apply error diffusion from chosen output pixel value
|
|
for i in range(3):
|
|
output_pixel_rgb[i] = palette_rgb[output_pixel_nbit, x % 4, i]
|
|
quant_error[i] = image_rgb[y,x,i] - output_pixel_rgb[i]
|
|
apply(&cdither, xres, yres, x, y, image_rgb, quant_error)
|
|
|
|
# Update image with our chosen image pixel
|
|
image_nbit[y, x] = output_pixel_nbit
|
|
for i in range(3):
|
|
image_rgb[y, x, i] = output_pixel_rgb[i]
|
|
|
|
free(cdither.pattern)
|
|
return image_nbit_to_bitmap(image_nbit, xres, yres, palette_depth)
|
|
|
|
import colour
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palettes_rgb, float[:,::1] rgb_to_cam16ucs):
|
|
cdef int y, x, idx, best_colour_idx, best_palette
|
|
cdef float best_distance, distance
|
|
cdef float[::1] best_colour_rgb, pixel_cam, colour_rgb, colour_cam
|
|
cdef float quant_error
|
|
cdef float[:, ::1] palette_rgb
|
|
|
|
cdef (unsigned char)[:, ::1] output_4bit = np.zeros((200, 320), dtype=np.uint8)
|
|
# cdef (unsigned char)[:, :, ::1] output_rgb = np.zeros((200, 320, 3), dtype=np.uint8)
|
|
|
|
cdef float[:, ::1] line_cam = np.zeros((320, 3), dtype=np.float32)
|
|
|
|
line_to_palette = {}
|
|
best_palette = 15
|
|
for y in range(200):
|
|
print(y)
|
|
# palette_rgb = palettes_rgb[line_to_palette[y]]
|
|
|
|
for x in range(320):
|
|
colour_cam = convert_rgb_to_cam16ucs(
|
|
rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
|
|
line_cam[x, :] = colour_cam
|
|
|
|
best_palette = best_palette_for_line(line_cam, palettes_cam, <int>(y * 16 / 200), best_palette)
|
|
# print("-->", best_palette)
|
|
palette_rgb = palettes_rgb[best_palette]
|
|
line_to_palette[y] = best_palette
|
|
|
|
for x in range(320):
|
|
pixel_cam = convert_rgb_to_cam16ucs(
|
|
rgb_to_cam16ucs, working_image[y, x, 0], working_image[y, x, 1], working_image[y, x, 2])
|
|
|
|
best_distance = 1e9
|
|
best_colour_idx = -1
|
|
for idx in range(16):
|
|
colour_rgb = palette_rgb[idx, :]
|
|
colour_cam = convert_rgb_to_cam16ucs(rgb_to_cam16ucs, colour_rgb[0], colour_rgb[1], colour_rgb[2])
|
|
distance = colour_distance_squared(pixel_cam, colour_cam)
|
|
if distance < best_distance:
|
|
best_distance = distance
|
|
best_colour_idx = idx
|
|
best_colour_rgb = palette_rgb[best_colour_idx]
|
|
output_4bit[y, x] = best_colour_idx
|
|
|
|
for i in range(3):
|
|
quant_error = working_image[y, x, i] - best_colour_rgb[i]
|
|
|
|
# Floyd-Steinberg dither
|
|
# 0 * 7
|
|
# 3 5 1
|
|
working_image[y, x, i] = best_colour_rgb[i]
|
|
if x < 319:
|
|
working_image[y, x + 1, i] = clip(
|
|
working_image[y, x + 1, i] + quant_error * (7 / 16), 0, 1)
|
|
if y < 199:
|
|
if x > 0:
|
|
working_image[y + 1, x - 1, i] = clip(
|
|
working_image[y + 1, x - 1, i] + quant_error * (3 / 32), 0, 1)
|
|
working_image[y + 1, x, i] = clip(
|
|
working_image[y + 1, x, i] + quant_error * (5 / 32), 0, 1)
|
|
if x < 319:
|
|
working_image[y + 1, x + 1, i] = clip(
|
|
working_image[y + 1, x + 1, i] + quant_error * (1 / 32), 0, 1)
|
|
|
|
# # 0 0 X 7 5
|
|
# # 3 5 7 5 3
|
|
# # 1 3 5 3 1
|
|
#if x < 319:
|
|
# working_image[y, x + 1, i] = clip(
|
|
# working_image[y, x + 1, i] + quant_error * (7 / 48), 0, 1)
|
|
#if x < 318:
|
|
# working_image[y, x + 2, i] = clip(
|
|
# working_image[y, x + 2, i] + quant_error * (5 / 48), 0, 1)
|
|
#if y < 199:
|
|
# if x > 1:
|
|
# working_image[y + 1, x - 2, i] = clip(
|
|
# working_image[y + 1, x - 2, i] + quant_error * (3 / 48), 0,
|
|
# 1)
|
|
# if x > 0:
|
|
# working_image[y + 1, x - 1, i] = clip(
|
|
# working_image[y + 1, x - 1, i] + quant_error * (5 / 48), 0,
|
|
# 1)
|
|
# working_image[y + 1, x, i] = clip(
|
|
# working_image[y + 1, x, i] + quant_error * (7 / 48), 0, 1)
|
|
# if x < 319:
|
|
# working_image[y + 1, x + 1, i] = clip(
|
|
# working_image[y + 1, x + 1, i] + quant_error * (5 / 48),
|
|
# 0, 1)
|
|
# if x < 318:
|
|
# working_image[y + 1, x + 2, i] = clip(
|
|
# working_image[y + 1, x + 2, i] + quant_error * (3 / 48),
|
|
# 0, 1)
|
|
#if y < 198:
|
|
# if x > 1:
|
|
# working_image[y + 2, x - 2, i] = clip(
|
|
# working_image[y + 2, x - 2, i] + quant_error * (1 / 48), 0,
|
|
# 1)
|
|
# if x > 0:
|
|
# working_image[y + 2, x - 1, i] = clip(
|
|
# working_image[y + 2, x - 1, i] + quant_error * (3 / 48), 0,
|
|
# 1)
|
|
# working_image[y + 2, x, i] = clip(
|
|
# working_image[y + 2, x, i] + quant_error * (5 / 48), 0, 1)
|
|
# if x < 319:
|
|
# working_image[y + 2, x + 1, i] = clip(
|
|
# working_image[y + 2, x + 1, i] + quant_error * (3 / 48),
|
|
# 0, 1)
|
|
# if x < 318:
|
|
# working_image[y + 2, x + 2, i] = clip(
|
|
# working_image[y + 2, x + 2, i] + quant_error * (1 / 48),
|
|
# 0, 1)
|
|
|
|
return np.array(output_4bit, dtype=np.uint8), line_to_palette
|
|
|
|
import collections
|
|
import random
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
def k_means_with_fixed_centroids(
|
|
int n_clusters, float[:, ::1] data, float[:, ::1] fixed_centroids = None,
|
|
int iterations = 10000, float tolerance = 1e-3):
|
|
cdef int i, iteration, centroid_idx, num_fixed_centroids, num_random_centroids, best_centroid_idx
|
|
cdef float[::1] point, centroid, new_centroid, old_centroid
|
|
cdef float[:, ::1] centroids
|
|
cdef float best_dist, centroid_movement, dist
|
|
|
|
centroids = np.zeros((n_clusters, 3), dtype=np.float32)
|
|
if fixed_centroids is not None:
|
|
centroids[:fixed_centroids.shape[0], :] = fixed_centroids
|
|
num_fixed_centroids = fixed_centroids.shape[0] if fixed_centroids is not None else 0
|
|
num_random_centroids = n_clusters - num_fixed_centroids
|
|
|
|
# TODO: kmeans++ initialization
|
|
cdef int rand_idx = random.randint(0, data.shape[0])
|
|
for i in range(num_random_centroids):
|
|
centroids[num_fixed_centroids + i, :] = data[rand_idx, :]
|
|
|
|
cdef int[::1] centroid_weights = np.zeros(n_clusters, dtype=np.int32)
|
|
for iteration in range(iterations):
|
|
# print("centroids ", centroids)
|
|
closest_points = collections.defaultdict(list)
|
|
for point in data:
|
|
best_dist = 1e9
|
|
best_centroid_idx = 0
|
|
for centroid_idx in range(n_clusters):
|
|
centroid = centroids[centroid_idx, :]
|
|
dist = colour_distance(centroid, point)
|
|
if dist < best_dist:
|
|
best_dist = dist
|
|
best_centroid_idx = centroid_idx
|
|
closest_points[best_centroid_idx].append(point)
|
|
|
|
centroid_movement = 0
|
|
for centroid_idx, points in closest_points.items():
|
|
centroid_weights[centroid_idx] = len(points)
|
|
if centroid_idx < num_fixed_centroids:
|
|
continue
|
|
new_centroid = np.median(np.array(points), axis=0)
|
|
old_centroid = centroids[centroid_idx]
|
|
centroid_movement += colour_distance(old_centroid, new_centroid)
|
|
centroids[centroid_idx, :] = new_centroid
|
|
# print("iteration %d: movement %f" % (iteration, centroid_movement))
|
|
if centroid_movement < tolerance:
|
|
break
|
|
|
|
weighted_centroids = list(zip(centroid_weights, [tuple(c) for c in centroids]))
|
|
print(weighted_centroids)
|
|
return np.array([c for w, c in sorted(weighted_centroids, reverse=True)], dtype=np.float32)
|
|
|
|
@cython.boundscheck(False)
|
|
@cython.wraparound(False)
|
|
cdef int best_palette_for_line(float [:, ::1] line_cam, object palettes_cam, int base_palette_idx, int last_palette_idx):
|
|
cdef int palette_idx, best_palette_idx
|
|
cdef float best_total_dist, total_dist, best_pixel_dist, pixel_dist
|
|
cdef float[:, ::1] palette_cam
|
|
cdef float[::1] pixel_cam, palette_entry
|
|
|
|
best_total_dist = 1e9
|
|
best_palette_idx = -1
|
|
for palette_idx, palette_cam in palettes_cam.items():
|
|
if palette_idx < (base_palette_idx - 1) or palette_idx > (base_palette_idx + 1):
|
|
continue
|
|
if palette_idx == last_palette_idx:
|
|
continue
|
|
total_dist = 0
|
|
best_pixel_dist = 1e9
|
|
for pixel_cam in line_cam:
|
|
for palette_entry in palette_cam:
|
|
pixel_dist = colour_distance_squared(pixel_cam, palette_entry)
|
|
if pixel_dist < best_pixel_dist:
|
|
best_pixel_dist = pixel_dist
|
|
total_dist += best_pixel_dist
|
|
# print(total_dist)
|
|
if total_dist < best_total_dist:
|
|
best_total_dist = total_dist
|
|
best_palette_idx = palette_idx
|
|
return best_palette_idx
|
|
|